1. Field
Example embodiments relate to an apparatus to extract feature points from an image and registering the extracted feature points for image-based localization, and a related method and computer-readable medium.
2. Description of the Related Art
To autonomously move, a robot needs to keep track of its current location within an unknown environment without a priori knowledge (localization), while at the same time, building up a map based on information about the environment (mapping). This is called Simultaneous Localization And Mapping (SLAM).
SLAM is an image-based localization technique that allows for real-time mapping and self-localization, based on images captured by an omni-directional camera. For the image-based localization, feature points are extracted from an image, the three-dimensional (3D) coordinates of the feature points are calculated, and then the feature points are registered as 3D feature points. Corner points are usually used as feature points in an image because the corners allow robust tracking when the image moves. To extract such 3D information, a stereo vision/Time Of Flight (TOF) camera is usually used. However, when 3D information about a corner is extracted, no values are acquired from the stereo vision/TOF camera in some cases. While the stereo vision/TOF camera relies on the disparity between two images to acquire 3D information, it may not get 3D information about corner points due to occlusion that occurs to a 3D object. In addition, the TOF camera suffers from diffraction at corners, making it difficult to identify corners accurately. Consequently, 3D information about the corners may not be gathered.